Satellite images, aerial images, and other geographic images, are available from a number of different sources, such as providers of satellite imagery for the purpose of earth visualization, map production and related purposes.
Geographic assets typically include both an image such as that mentioned above, and an identification of where the image may be found. For example, an asset may be associated with both a digitized image and geo-location data, that is, the relative position of the image pixels with respect to a geographical reference point. Geo-location data is typically expressed in terms of longitude and latitude.
It is helpful in many circumstances to simultaneously use information from two different assets. For example, a user may wish to view two different countries at a time, where each country is captured in a different asset. There may also be value in superimposing an image from one asset on top of another asset for the purpose of rendering a composite image or other processing.
Unfortunately, asset geo-location data is often subject to some amount of error. For example, even if two assets had identical geo-location data, the images associated with the assets may depict somewhat different geographic locations. Processing two assets based solely on their geo-location data may thus result in errors. This difference in geo-location data may be due to any number of factors, such as uncorrected errors in the recorded position and/or pose of the imaging equipment, or inaccuracies in the data or processes used to correct for photogrammetric distortions caused by terrain and man-made structures.
Previous attempts to overcome the problem have included manually “stitching” assets together. For example, a human operator may view pairs of misaligned images of the same general geographic area, and use software tools to identify specific points in one image that correspond to specific points in the other image. Distinctive visual features such as mountain peaks and road intersections are often used for this purpose. The process can be semi-automated in that the software can automatically extract candidate tie-points and offer them to the operator for view. Such software includes ERDAS Imagine from Leica Geosystems. The operator can also specify the type of deformation that ought to be applied in order to align the images. The software then automatically produces a possible alignment for the images using standard programmatic techniques such as bundle adjustment. The result is then reviewed by a human for quality control and, if it is not acceptable, the operator may provide additional tie points or specify the use of a different deformation.
This process is laborious, slow, requires skilled operators, is error prone, and impractical for use in connection with large numbers of assets.
There is a need for a system and method that mitigates the foregoing problems.